{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import bambi as bmb\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count Regression with Variable Exposure\n",
    "\n",
    "This example is based on the [\"Roaches\"](https://github.com/avehtari/ROS-Examples/tree/master/Roaches/) example from [Regression and Other Stories](https://avehtari.github.io/ROS-Examples/index.html) by Gelman, Hill, and Vehtari.   The example is a count regression model with an offset term. \n",
    "\n",
    "The data is the number of roaches caught in 262 apartments.  Some pest control treatment was applied to 158 (treatment=1) of the apartments, and 104 apartments received no treatment (treatment=0).   The other columns in the data are:\n",
    "\n",
    "- `y`: the number of roaches caught\n",
    "- `roach1` : the pre-treatment roach level\n",
    "- `senior` : indicator for whether the appartment is for seniors\n",
    "- `exposure2` : the number of trap-days (number of traps x number of days).   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>roach1</th>\n",
       "      <th>treatment</th>\n",
       "      <th>senior</th>\n",
       "      <th>exposure2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153</td>\n",
       "      <td>3.0800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>127</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y  roach1  treatment  senior  exposure2\n",
       "1  153  3.0800          1       0   0.800000\n",
       "2  127  3.3125          1       0   0.600000\n",
       "3    7  0.0167          1       0   1.000000\n",
       "4    7  0.0300          1       0   1.000000\n",
       "5    0  0.0200          1       0   1.142857"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roaches = pd.read_csv(\"data/roaches.csv\", index_col=0)\n",
    "# rescale \n",
    "roaches[\"roach1\"] = roaches[\"roach1\"] / 100\n",
    "roaches.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poisson regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "One way to model this is to say that there is some rate of roaches per trap-day , and that the number of roaches caught is a Poisson random variable with a rate that is proportional to the number of trap-days (the *exposure*).    That is:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "y_i &\\sim \\text{Poisson}(\\text{exposure2}_i \\times \\rho_i) \\\\\n",
    "\\log(\\rho_i) &= \\beta_0 + \\beta_1 \\text{treatment}_i + \\beta_2 \\text{roach1}_i + \\beta_3 \\text{senior}_i\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "With a little algebra, we can rewrite this as a generalized linear model:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "y_i &\\sim \\text{Poisson}(\\lambda_i) \\\\\n",
    "\\log(\\lambda_i) &= \\beta_0 + \\beta_1 \\text{treatment}_i + \\beta_2 \\text{roach1}_i + \\beta_3 \\text{senior}_i + \\log(\\text{exposure2}_i)\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "However, we don't want to estimate a coefficient for $\\log(\\text{exposure2})$, we want to simply add it as an *offset*.   In `bambi` we do this by using the `offset` function in the formula to specify that a term should not be multiplied by a coefficient to estimate and simply added.  The formula for the model is then:\n",
    "\n",
    "```python\n",
    "\"y ~ roach1 + treatment + senior + offset(log(exposure2))\"\n",
    "```\n",
    "\n",
    "If you are familiar with R this offset term is the same as the offset term in the `glm` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Intercept, roach1, treatment, senior]\n",
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bdef8c47c0140b5a1dc168d3619ab68",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n",
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n",
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model_1 = bmb.Model(\n",
    "    \"y ~ roach1 + treatment  + senior + offset(log(exposure2))\",\n",
    "    family=\"poisson\",\n",
    "    data=roaches\n",
    ")\n",
    "idata_1 = model_1.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>3.089</td>\n",
       "      <td>0.021</td>\n",
       "      <td>3.047</td>\n",
       "      <td>3.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3984.0</td>\n",
       "      <td>3168.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roach1</th>\n",
       "      <td>0.698</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3244.0</td>\n",
       "      <td>2652.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <td>-0.516</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.561</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4011.0</td>\n",
       "      <td>3088.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>-0.381</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.443</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4217.0</td>\n",
       "      <td>3239.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "Intercept  3.089  0.021   3.047    3.126      0.000      0.0    3984.0   \n",
       "roach1     0.698  0.009   0.682    0.715      0.000      0.0    3244.0   \n",
       "treatment -0.516  0.025  -0.561   -0.467      0.000      0.0    4011.0   \n",
       "senior    -0.381  0.034  -0.443   -0.317      0.001      0.0    4217.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "Intercept    3168.0    1.0  \n",
       "roach1       2652.0    1.0  \n",
       "treatment    3088.0    1.0  \n",
       "senior       3239.0    1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampling seems to have gone well based on `ess` and `r_hat`.  If this were a real analysis one would also need to check priors, trace plots and other diagnostics.  However, let's see if the model predicts the distribution of roaches (`y`) observed. We can do this by looking at  the posterior predictive distribution for the model.  We plot the log of `y` to make the results easier to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_log_posterior_ppc(model, idata):\n",
    "    # plot posterior predictive check\n",
    "    model.predict(idata, kind=\"response\", inplace=True)\n",
    "    var_name = \"log(y+1)\"\n",
    "    # there is probably a better way\n",
    "    idata.posterior_predictive[var_name] = np.log(idata.posterior_predictive[\"y\"] + 1)\n",
    "    idata.observed_data[var_name] = np.log(idata.observed_data[\"y\"] + 1)\n",
    "    return az.plot_ppc(idata, var_names=[var_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_log_posterior_ppc(model_1, idata_1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that we are drastically under predicting the number of apartments with a small number of roaches.  This suggests creating a test statistic measuring the fraction of zeros, both in the observed data and in the simulated replications (posterior predictions).  We can then use this to check the model fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of zeros in the observed data: 0.35877862595419846\n",
      "Fraction of zeros in the posterior predictive check: 0.0006908396946564885\n",
      " 80% CI: [0.         0.00381679]\n"
     ]
    }
   ],
   "source": [
    "# check number of zeros in y\n",
    "def check_zeros(idata):\n",
    "    # flatten over chains:\n",
    "    sampled_zeros = (idata.posterior_predictive[\"y\"]==0).mean((\"__obs__\")).values.flatten()\n",
    "    print(f\"Fraction of zeros in the observed data: {np.mean(roaches['y']==0)}\")\n",
    "    print(f\"Fraction of zeros in the posterior predictive check: {np.mean(sampled_zeros)}\")\n",
    "    print(f\" 80% CI: {np.percentile(sampled_zeros, [10, 90])}\")\n",
    "\n",
    "check_zeros(idata_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Poisson model here does not succeed in reproducing the observed fraction of zeros. In the data we have about 36% zeros, while in the replications we almost always have no zeros or very few.  Gelman, Hill, and Vehtari suggest we try an overdispersed and more flexible model like the negative binomial.   \n",
    "\n",
    "## Negative Binomial Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, Intercept, roach1, treatment, senior]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51fb7984be204a65953be0f8b91b2009",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "model_2 = bmb.Model(\n",
    "    \"y ~ roach1 + treatment  + senior + offset(log(exposure2))\", \n",
    "    family=\"negativebinomial\",\n",
    "    data=roaches\n",
    ")\n",
    "idata_2 = model_2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.272</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5974.0</td>\n",
       "      <td>3132.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2.851</td>\n",
       "      <td>0.237</td>\n",
       "      <td>2.414</td>\n",
       "      <td>3.293</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>5478.0</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roach1</th>\n",
       "      <td>1.322</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.848</td>\n",
       "      <td>1.799</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4698.0</td>\n",
       "      <td>3254.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <td>-0.783</td>\n",
       "      <td>0.251</td>\n",
       "      <td>-1.238</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>4998.0</td>\n",
       "      <td>3330.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>-0.324</td>\n",
       "      <td>0.260</td>\n",
       "      <td>-0.813</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>5572.0</td>\n",
       "      <td>3363.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha      0.272  0.026   0.227    0.324      0.000    0.000    5974.0   \n",
       "Intercept  2.851  0.237   2.414    3.293      0.003    0.004    5478.0   \n",
       "roach1     1.322  0.253   0.848    1.799      0.004    0.004    4698.0   \n",
       "treatment -0.783  0.251  -1.238   -0.300      0.004    0.004    4998.0   \n",
       "senior    -0.324  0.260  -0.813    0.160      0.003    0.004    5572.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "alpha        3132.0    1.0  \n",
       "Intercept    3249.0    1.0  \n",
       "roach1       3254.0    1.0  \n",
       "treatment    3330.0    1.0  \n",
       "senior       3363.0    1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_log_posterior_ppc(model_2, idata_2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of zeros in the observed data: 0.35877862595419846\n",
      "Fraction of zeros in the posterior predictive check: 0.3383148854961832\n",
      " 80% CI: [0.29007634 0.38931298]\n"
     ]
    }
   ],
   "source": [
    "check_zeros(idata_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_zeros(ax, idata, model_label, **kwargs):\n",
    "    data_zeros = np.mean(roaches['y']==0)\n",
    "    # flatten over chains:\n",
    "    sampled_zeros = (idata.posterior_predictive[\"y\"]==0).mean((\"__obs__\")).values.flatten()\n",
    "    ax.hist(sampled_zeros, alpha=0.5, **kwargs)\n",
    "    ax.axvline(data_zeros, color='red', linestyle='--')\n",
    "    ax.set_xlabel(\"Fraction of zeros\")\n",
    "    ax.set_title(f\"Model: {model_label}\")\n",
    "    ax.yaxis.set_visible(False)\n",
    "    ax.set_facecolor('white')\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots(1,2, gridspec_kw={'wspace': 0.2})\n",
    "plot_zeros(ax[0],idata_1, \"Poisson\", bins= 2) # use 2 bins to make it more clear. Almost no zeros.\n",
    "plot_zeros(ax[1],idata_2, \"Negative Binomial\")\n",
    "\n",
    "fig.legend([\"Observed data\", \"Posterior predictive\"], loc='center left', bbox_to_anchor=(0.05, 0.8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The negative binomial distribution fit works much better, predicting the number of zeros consistent with the observed data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    " *Regression and Other Stories* introduces a further improvement by introducing a zero-inflated regression later in the chapter, but I will not persue that here, after all the point of this example is to illustrate the use of offsets. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyMC equivalent model\n",
    "\n",
    "The model behind the scenes looks like this for the Poission model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "            \\begin{array}{rcl}\n",
       "            \\text{Intercept} &\\sim & \\operatorname{Normal}(0,~4.52)\\\\\\text{roach1} &\\sim & \\operatorname{Normal}(0,~3.33)\\\\\\text{treatment} &\\sim & \\operatorname{Normal}(0,~5.11)\\\\\\text{senior} &\\sim & \\operatorname{Normal}(0,~5.43)\\\\\\text{mu} &\\sim & \\operatorname{Deterministic}(f(\\text{Intercept},~\\text{senior},~\\text{treatment},~\\text{roach1}))\\\\\\text{y} &\\sim & \\operatorname{Poisson}(\\text{mu})\n",
       "            \\end{array}\n",
       "            $$"
      ],
      "text/plain": [
       "Intercept ~ Normal(0, 4.52)\n",
       "   roach1 ~ Normal(0, 3.33)\n",
       "treatment ~ Normal(0, 5.11)\n",
       "   senior ~ Normal(0, 5.43)\n",
       "       mu ~ Deterministic(f(Intercept, senior, treatment, roach1))\n",
       "        y ~ Poisson(mu)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pymc_model = model_1.backend\n",
    "pymc_model.model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's look at the equivalent (Poisson) model in PYMC:   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Intercept, beta_roach1, beta_treatment, beta_senior]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63e7aaff6b46423fabfdf1ce3f48ad1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n",
      "/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>3.089</td>\n",
       "      <td>0.021</td>\n",
       "      <td>3.051</td>\n",
       "      <td>3.129</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2865.0</td>\n",
       "      <td>2564.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_roach1</th>\n",
       "      <td>0.698</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3031.0</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_treatment</th>\n",
       "      <td>-0.516</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.562</td>\n",
       "      <td>-0.469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2716.0</td>\n",
       "      <td>2597.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_senior</th>\n",
       "      <td>-0.380</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>-0.314</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3109.0</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "Intercept       3.089  0.021   3.051    3.129      0.000    0.000    2865.0   \n",
       "beta_roach1     0.698  0.009   0.681    0.714      0.000    0.000    3031.0   \n",
       "beta_treatment -0.516  0.025  -0.562   -0.469      0.000    0.000    2716.0   \n",
       "beta_senior    -0.380  0.034  -0.440   -0.314      0.001    0.001    3109.0   \n",
       "\n",
       "                ess_tail  r_hat  \n",
       "Intercept         2564.0    1.0  \n",
       "beta_roach1       2947.0    1.0  \n",
       "beta_treatment    2597.0    1.0  \n",
       "beta_senior       2771.0    1.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pymc as pm\n",
    "\n",
    "with pm.Model() as model_pymc:\n",
    "    # priors\n",
    "    alpha = pm.Normal(\"Intercept\", mu=0, sigma=4.5)\n",
    "    beta_roach1 = pm.Normal(\"beta_roach1\", mu=0, sigma=3.3)\n",
    "    beta_treatment = pm.Normal(\"beta_treatment\", mu=0, sigma=5.11)\n",
    "    beta_senior = pm.Normal(\"beta_senior\", mu=0, sigma=5.43)\n",
    "\n",
    "    # likelihood\n",
    "    # see no beta for exposure2\n",
    "    mu = pm.math.exp(\n",
    "        alpha\n",
    "        + beta_roach1 * roaches[\"roach1\"]\n",
    "        + beta_treatment * roaches[\"treatment\"]\n",
    "        + beta_senior * roaches[\"senior\"]\n",
    "        + pm.math.log(roaches[\"exposure2\"])\n",
    "    )\n",
    "\n",
    "    y = pm.Poisson(\"y\", mu=mu, observed=roaches[\"y\"])\n",
    "\n",
    "    idata_pymc = pm.sample(1000)\n",
    "\n",
    "az.summary(idata_pymc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this model (`model_pymc`) we have the equivalent Poisson regression with everything explicit to illustrate what the 'offset' function is doing. It simply makes it possible to express a term like this in the `formulae` string in a `bambi` model.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sun Sep 28 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.7\n",
      "IPython version      : 9.4.0\n",
      "\n",
      "matplotlib: 3.10.6\n",
      "arviz     : 0.22.0\n",
      "bambi     : 0.14.1.dev56+gd93591cd2.d20250927\n",
      "pymc      : 3.9.2+2907.g7a3db78e6\n",
      "numpy     : 2.3.3\n",
      "pandas    : 2.3.2\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  }
 ],
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